Parent Perceptions of NutriSTEP and Nutrition Risk Screening in a Sample of Ontario Preschool Children
Bibliographic record
Abstract
Nutrition screening with NutriSTEP (Nutrition Screening Tool for Every Preschooler) requires parents to complete a 17-item, self-administered questionnaire and follow through with appropriate recommendations. Parents’ perceptions of screening and referral procedures can be used to evaluate the process of an ethical child nutrition screening program. Targeted implementation of NutriSTEP took place in 3 diverse Ontario communities using an assisted or self-referral model. Of the 364 respondents, 264 parents consented to a follow-up telephone interview, with 164 (62%) interviews conducted in English or French. The interview focused on parents’ reasons for participating, their perspectives of screening and referral processes, and any perceived barriers to, concerns about, and benefits of screening. Most parents reported that screening confirmed their perception of risk; reminded them about healthy preschool diets and areas to improve; increased their nutrition awareness, motivation, and confidence; and informed them about additional information sources. Some barriers to seeking referrals or further education included not being concerned, no time for referrals or dietary changes, and not knowing where to go for more information. Overall, nutrition risk screening using NutriSTEP was well accepted, yet referral uptake was modest. Efforts are needed to encourage parents of children at risk to follow through with recommendations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".